Feasibility study of deep learning based on data-based e-commerce operations

In this paper, we first study data-based e-commerce operation from the big data perspective and elaborate on data-based e-commerce operation from four aspects: professional terminology, operation core, theoretical operation basis, and operation method. Secondly, in the process of e-commerce data-based operation sales prediction, data pre-processing and feature selection are required, and the quality of data and features directly determines the accuracy of the model and based on deep learning, the structure of convolutional and XG fusion prediction model is proposed. Then, three-dimensional data frames are constructed based on four-dimensional data facets, which leads to more accurate model predictiveness, and the prediction model and e-commerce financial and operational risks are studied and analyzed. The results show that the convolutional and XG fusion forecasting model has a greater efficiency enhancement function than the traditional data computation model in the practical application of the forecasting model, proving the model's stability in the long-term forecasting process. By predicting risk in e-commerce operations in time and improving it in real-time, this study can help the enterprise develop more competitively. © 2024 Applied Mathematics and Nonlinear Sciences. All rights reserved.